Greedy Heuristics Adapted for the Multi-commodity Pickup and Delivery Traveling Salesman Problem
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The Multi-Commodity One-to-One Pickup and Delivery Traveling Salesman Problem finds the optimal tour that transports a set of unique commodities from their pickup to delivery locations, while never exceeding the maximum payload capacity of the material handling agent. For this NP hard problem, this paper presents adaptations of the nearest neighbor and cheapest insertion heuristics to account for the constraints related to the precedence between the locations and the cargo capacity limitations. To test the effectiveness of the proposed algorithms, the well-known TSPLIB benchmark data-set is modified in a replicable manner to create precedence constraints, while varying the cargo capacity of the agent. It is seen that the adapted Nearest Neighbor heuristic outperforms the adapted Cheapest Insertion algorithm in the majority of the cases studied, while providing near instantaneous solutions.
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